This work employs the application of three artificial intelligence (AI) techniques namely; support vector machine (SVM), Hammerstein-Wiener (HW) and multi-layer perceptron (MLP) for predicting the qualitative properties of an anti-Alzheimer agent using high-pressure liquid chromatography technique. The mobile phase (inform of acetonitrile and trifluoroacetic acid) and the column temperature was used as the predictors in modelling the maximum retention time (tR-max) and resolution (Resol.) as the output variables of the analyte. The measured and predicted values were checked using three performance indices including; Nash–Sutcliffe efficiency (NSE), correlation coefficient (CC) as the goodness of fits and a statistical error inform of root-mean-square error (RMSE). The results obtained demonstrated the promising ability of AI-based models in modelling the qualitative properties of the anti-Alzheimer agent. Observation of different outputs of the AI-based models at various time intervals showed the necessity of ensembling the outputs of the AI-based models. Therefore, simple average ensemble and support vector machine ensemble (SVM-E) were employed to enhance the performance skills of the simple models. The comparative performance of SVM-E inform of NSE indicated its ability in boosting and enhancing the performance skills of the single models SVM, MLP and HW models up to 5, 13 and 20% respectively in the testing stage for modelling tR-max.

The antidiabetic principles from methanolic leaf extract of Bryophyllum pinnatum were isolated. The leaf extract was fractionated on silica gel using column chromatography and identified using nuclear magnetic resonance (NMR) spectrometry. The ethylacetate fraction of the partitioned methanolic extract of B. pinnatum lowered blood glucose of alloxan‐induced diabetic rats and inhibited α‐amylase and α‐glucosidase with IC₅₀ 137.89 and 110.15 µg/mL, respectively. In addition to lowering blood glucose, fractions A and B inhibited α‐amylase with IC₅₀ 57.43 and 43.84 µg/mL and α‐glucosidase with IC₅₀ 11.15 and 25.79 µg/mL, respectively. ¹H and ¹³C NMR showed that fractions A and B are quercetin and kaempeferol, respectively. Molecular docking revealed that kaempferol and quercetin interacted with amino acid residues that bind/hydrolyze substrate molecules These compounds reversed altered lipid profile and oxidative stress biomarkers. Our findings showed that kaempferol and quercetin are responsible for the antidiabetic activity of B. pinnatum. Practical application Bryophyllum pinnatum is an edible vegetable plant in some parts of Nigeria, and its consumption could improve diabetic condition and lower postprandial glucose. Furthermore, extract of the leaves could be developed into food supplements for managing diabetes and its associated complications including dyslipidaemia and oxidative stress.

Breast cancer is a common cancer affecting women worldwide, and it progresses from breast tissue to other parts of the body through a process called metastasis. Albizia lebbeck is a valuable plant with medicinal properties due to some active biological macromolecules, and it’s cultivated in subtropical and tropical regions of the world. This study reports the phytochemical compositions, the cytotoxic, anti-proliferative and anti-migratory potential of A. lebbeck methanolic (ALM) extract on strongly and weakly metastatic MDA-MB 231 and MCF-7 human breast cancer cells, respectively. Furthermore, we employed and compared an artificial neural network (ANN), an adaptive neuro-fuzzy inference system (ANFIS), and multilinear regression analysis (MLR) to predict cell migration on the treated cancer cells with various concentrations of the extract using our experimental data. Lower concentrations of the ALM extract (10, 5 & 2.5 μg/mL) showed no significant effect. Higher concentrations (25, 50, 100 & 200 μg/mL) revealed a significant effect on the cytotoxicity and proliferation of the cells when compared with the untreated group (p < 0.05; n ≥ 3). Furthermore, the extract revealed a significant decrease in the motility index of the cells with increased extract concentrations (p < 0.05; n ≥ 3). The comparative study of the models observed that both the classical linear MLR and AI-based models could predict metastasis in MDA-MB 231 and MCF-7 cells. Overall, various ALM extract concentrations showed promising an-metastatic potential in both cells, with increased concentration and incubation period. The outcomes of MLR and AI-based models on our data revealed the best performance. They will provide future development in assessing the anti-migratory efficacies of medicinal plants in breast cancer metastasis.

Cancer is one of the major causes of death in the modern world, and the incidence varies considerably based on race, ethnicity, and region. Novel cancer treatments, such as surgery and immunotherapy, are ineffective and expensive. In this situation, ion channels responsible for cell migration have appeared to be the most promising targets for cancer treatment. This research presents findings on the organic compounds present in Albizia lebbeck ethanolic extracts (ALEE), as well as their impact on the anti-migratory, anti-proliferative and cytotoxic potentials on MDA-MB 231 and MCF-7 human breast cancer cell lines. In addition, artificial intelligence (AI) based models, multilayer perceptron (MLP), extreme gradient boosting (XGB), and extreme learning machine (ELM) were performed to predict in vitro cancer cell migration on both cell lines, based on our experimental data. The organic compounds composition of the ALEE was studied using gas chromatography-mass spectrometry (GC–MS) analysis. Cytotoxicity, anti-proliferations, and anti-migratory activity of the extract using Tryphan Blue, MTT, and Wound Heal assay, respectively. Among the various concentrations (2.5–200 μg/mL) of the ALEE that were used in our study, 2.5–10 μg/mL revealed anti-migratory potential with increased concentrations, and they did not show any effect on the proliferation of the cells (P < 0.05; n ≥ 3). Furthermore, the three data-driven models, Multi-layer perceptron (MLP), Extreme gradient boosting (XGB), and Extreme learning machine (ELM), predict the potential migration ability of the extract on the treated cells based on our experimental data. Overall, the concentrations of the plant extract that do not affect the proliferation of the type cells used demonstrated promising effects in reducing cell migration. XGB outperformed the MLP and ELM models and increased their performance efficiency by up to 3% and 1% for MCF and 1% and 2% for MDA-MB231, respectively, in the testing phase.

Background: Hormone production by the thyroid gland is a prime aspect of maintaining body homeostasis. In this study, the ability of single artificial intelligence (AI)-based models, namely multi-layer perceptron (MLP), support vector machine (SVM), and Hammerstein–Weiner (HW) models, were used in the simulation of thyroidism status. The study's primary aim is to unveil the best performing model for the simulation of thyroidism status using hepatic enzymes and hormones as the independent variables. Three statistical metrics were used in evaluating the performance of the models, namely determination coefficient (R²), correlation coefficient (R), and mean squared error (MSE). Results: Considering the quantitative and visual presentation of the results obtained, it has been observed that the MLP model showed higher performance skills than SVM and HW, which improved their performances up to 3.77% and 12.54%, respectively, in the testing stages. Furthermore, to boost the performance of the single AI-based models, three different ensemble approaches were employed, including neural network ensemble (NNE), weighted average ensemble (WAE), and simple average ensemble (SAE). The quantitative predictive performance of the NNE technique boosts the performance of SAE and WAE approaches up to 2.85% and 1.22%, respectively, in the testing stage. Conclusions: Comparative performance of the ensemble techniques over the single models showed that NNE outperformed all the three AI-based models (MLP, SVM, and HW) and boosted their performance accuracy up to 7.44%, 11.212%, and 19.98%, respectively, in the testing stages.

Amaç: Bu derlemede, farmasötik ürünlerin kalite kontrollerinin değerlendirilmesi ve belirlenmesi için portatif ve minyatür Fourier transform infrared (FT-IR), yakın kızıl ötesi (NIR) ve Raman spektrometrelerin geniş uygulama alanları tartışılmıştır. Farmasötik ürünlerde yapılan sahtecilikler sonucunda, genel refah için bir tehlike haline gelen taklit, hileli, tahrif edilmiş ve standart altı farmasötik ürünlerin belirlenmesinde kullanılan bu portatif spektrometreler, titreşimsel ve saçılma spektroskopileri için kullanılmaktadır. Gereç ve Yöntem: Çeşitli bilimsel makaleler ve kitaplar incelenmiş olup, geçmişten günümüze kadar olan süreçte, Fourier transform infrared (FT-IR), yakın kızıl ötesi (NIR) ve Raman spektrometrelerin farmasötik ürünlerin kalite kontrolü ve denetimi ile ilgili uygulamaları derlenmiştir. Sonuç ve Tartışma: Hileli farmasötik ürünler özellikle gelişmekte olan ülkeler için önemli bir tehdit haline gelmiştir. Bu problem, farmasötik ürünlere olan güveni azaltabilmektedir. Bu makalede bahsi geçen portatif cihazların uygulanması, fitöfarmasötiklerin kalite kontrolünde tekniklerin daha erişilebilir, hızlı, doğru, basit, hassas, sağlam ve daha da önemlisi, verimli olmasını sağlamaktadır.